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1. ORCHESTRATING BULK DATA TRANSFERS ACROSS
GEO-DISTRIBUTED DATACENTERS
Abstract—As it has become the norm for cloud providers to host multiple datacenters around the globe, significant
demands exist for inter-datacenter data transfers in large volumes, e.g., migration of big data. A challenge arises on
how to schedule the bulk data transfers at different urgency levels, in order to fully utilize the available inter-
datacenter bandwidth. The Software Defined Networking (SDN) paradigm has emerged recently which decouples
the control plane from the data paths, enabling potential global optimization of data routing in a network. This paper
aims to design a dynamic, highly efficient bulk data transfer service in a geo-distributed datacenter system, and
engineer its design and solution algorithms closely within an SDN architecture. We model data transfer demands as
delay tolerant migration requests with different finishing deadlines. Thanks to the flexibility provided by SDN, we
enable dynamic, optimal routing of distinct chunks within each bulk data transfer (instead of treating each transfer as
an infinite flow), which can be temporarily stored at intermediate datacenters to mitigate bandwidth contention with
more urgent transfers. An optimal chunk routing optimization model is formulated to solve for the best chunk
transfer schedules over time. To derive the optimal schedules in an online fashion, three algorithms are discussed,
namely a bandwidth-reserving algorithm, a dynamically -adjusting algorithm, and a future-demandfriendly
algorithm, targeting at different levels of optimality and scalability. We build an SDN system based on the Beacon
platformand OpenFlow APIs, and carefully engineer our bulk data transfer algorithms in the system. Extensive real-
world experiments are carried out to compare the three algorithms as well as those from the existing literature, in
terms of routing optimality, computational delay and overhead.
EXISTING SYSTEM:
In the network inside a data center, TCP congestion control and FIFO flow scheduling are currently used for data
flow transport, which are unaware of flow deadlines. A number of proposals have appeared for deadline-aware
congestion and rate control. D3 [12] exploits deadline information to control the rate at which each source host

2. introduces traffic into the network, and apportions bandwidth at the routers along the paths greedily to satisfy as
many deadlines as possible. D2TCP [13] is a Deadline-Aware Datacenter TCP protocol to handle bursty flows with
deadlines. A congestion avoidance algorithm is employed, which uses ECN feedback from the routers and flow
deadlines to modify the congestion window at the sender.In pFabric [14], switches implement simple prioritybased
scheduling/dropping mechanisms, based on a priority number carried in the packets of each flow, and each flow
starts at the line rate which throttles back only when high and persistent packet loss occurs. Differently, our work
focuses on transportation of bulk flows among datacenters in a geodistributed cloud. Instead of end-to-end
congestion control, we enable store-and-forward in intermediate datacenters, such that a source data center can send
data out as soon as the first-hop connection bandwidth allows, whereas intermediate datacenters can temporarily
store the data if more urgent/important flows need the next-hop link bandwidths.
PROPOSED SYSTEM:
This paper proposes a novel optimization model for dynamic, highly efficient scheduling of bulk data transfers in a
geo-distributed datacenter system, and engineers its design and solution algorithms practically within an OpenFlow-
based SDN architecture. We model data transfer requests as delay tolerant data migration tasks with different
finishing deadlines. Thanks to the flexibility of transmission scheduling provided by SDN, we enable dynamic,
optimal routing of distinct chunks within each bulk data transfer (instead of treating each transfer as an infinite
flow), which can be temporarily stored at intermediate datacenters and transmitted only at carefully scheduled times,
to mitigate bandwidth contention among tasks of different urgency levels. Our contributions are summarized as
follows. First, we formulate the bulk data transfer problem into a novel, optimal chunk routing problem, which
maximizes the aggregate utility gain due to timely transfer completions before the specified deadlines. Such an
optimization model enables flexible, dynamic adjustment of chunk transfer schedules in a systemwith dynamically-
arriving data transfer requests, which is impossible with a popularly-modeled flow-based optimal routing model.
Second, we discuss three dynamic algorithms to solve the optimal chunk routing problem, namely a bandwidth-
reserving algorithm, a dynamically-adjusting algorithm, and a futuredemand- friendly algorithm. These solutions are
targeting at different levels of optimality and computational complexity. Third, we build an SDN system based on
the OpenFlow APIs and Beacon platform [11], and carefully engineer our bulk data transfer algorithms in the

3. system. Extensive realworld experiments with real network traffic are carried out to compare the three algorithms as
well as those in the existing literature, in terms of routing optimality, computational delay and overhead.
Module 1
Orchestrating in Cloud computing
The goal of cloud orchestration is to, insofar as is possible, automate the configuration, coordination and
management of software and software interactions in such an environment. The process involves automating
workflows required for service delivery. Tasks involved include managing server runtimes, directing the flow of
processes among applications and dealing with exceptions to typical workflows. Vendors of cloud orchestration
products include Eucalyptus, Flexiant, IBM, Microsoft, VMware and V3 Systems. The term “orchestration”
originally comes from the study of music, where it refers to the arrangement and coordination of instruments for a
given piece.
Module 2
SDN-based Architecture
We consider a cloud spanning multiple datacenters located in different geographic locations (Fig. 1). Each
datacenter is connected via a core switch to the other datacenters. The connections among the datacenters are
dedicated, fullduplex links, either through leading tier-1 ISPs or private fiber networks of the cloud provider,
allowing independent and simultaneous two-way data transmissions. Data transfer requests may arise from each

4. datacenter to move bulk volumes of data to another datacenter. A gateway server is connected to the core switch in
each datacenter, responsible for aggregating cross-datacenter data transfer requests from the same datacenter, as
well as for temporarily storing data from other datacenters and forwarding them via the switch. It also tracks
network topology and bandwidth availability among the datacenters with the help of the switches. Combined closely
with the SDN paradigm, a central controller is deployed to implement the optimal data transfer algorithms,
dynamically configure the flow table on each switch, and instruct the gateway servers to store or to forward each
data chunk. The layered architecture we present realistically resembles B4 [9], which was designed and deployed by
Google for their G-scale inter-datacenter network: the gateway server plays a similar role of the site controller layer,
the controller corresponds well to the global layer, and the core switch at each location can be deemed as the per-site
switch clusters in B4.
Module 3
Dynamic algorithms
We present three practical algorithms which make job acceptance and chunk routing decisions in each time slot, and
achieve different levels of optimality and scalability.
The Bandwidth-Reserving Algorithm The first algorithm honors decisions made in previous time slots, and
reserves bandwidth along the network links for scheduled chunk transmissions of previously accepted jobs in its
routing computation for newly arrived jobs. Let J(_ ) be the set consisting of only the latest data transfer requests
arrived in time slot _ . Define Bm;n(t) as the residual bandwidth on each connection (m; n) in time slot t 2 [_ +1; �],
excluding bandwidth needed for the remaining chunk transfers of accepted jobs arrived before _ . In each time slot _
, the algorithm solves optimization (1) with job set J(_ ) and bandwidth Bm;n(t)’s for duration [_ +1; �], and derives
admission control decisions for jobs arrived in this time slot, as well as their chunk transfer schedules before their
respective deadlines. Theorem 1 states the NP-hardness of optimization problem in (1) (with detailed proof in
AppendixA). Nevertheless, such a linear integer program may still be solved in reasonable time at a typical scale of
the problem (e.g., tens of datacenters in the system), using an optimization tool such as CPLEX [26]. To cater for
larger scale problems, we also propose a highly efficient heuristic. .
The Dynamically-Adjusting Algorithm

5. The second algorithm retains job acceptance decisions made in previous time slots, but adjusts routing schedules for
chunks of accepted jobs, which have not reached their respective destinations, together with the admission control
and routing computation of newly arrived jobs. Let J(_ ) be the set of data transfer requests arrived in time slot _ ,
and J(_�) represent the set of unfinished, previously accepted jobs by time slot _ . In each time slot _ , the algorithm
solves a modified version of optimization (1)
Module 4
The Future-Demand-Friendly Heuristic
We further propose a simple but efficient heuristic to make job acceptance and chunk routing decisions in each time
slot, with polynomial-time computational complexity, suitable for systems with larger scales. Similar to the first
algorithm, the heuristic retains routing decisions computed earlier for chunks of already accepted jobs, but only
makes decisions for jobs received in this time slot using the remaining bandwidth. On the other hand, it is more
future demand friendly than the first algorithm, by postponing the transmission of accepted jobs as much as possible,
to save bandwidth available in the immediate future in case more urgent transmission jobs may arrive. Let J(_ ) be
the set of latest data transfer requests arrived in time slot _ . The heuristic is given in Alg. 1. At the job level, the
algorithm preferably handles data transfer requests with higher weights and smaller sizes (line 1), i.e., larger weight
per unit bandwidth consumption. For each chunk in job J, the algorithm chooses a transfer path with the fewest
number of hops, that has available bandwidth to forward the chunk from the source to the destination before the
deadline.
CONCLUSION
This paper presents our efforts to tackle an arising challenge in geo-distributed datacenters, i.e., deadline-aware bulk
data transfers. Inspired by the emerging Software Defined Networking (SDN) initiative that is well suited to
deployment of an efficient scheduling algorithm with the global view of the network, we propose a reliable and
efficient underlying bulk data transfer service in an inter-datacenter network, featuring optimal routing for distinct

6. chunks over time, which can be temporarily stored at intermediate datacenters and forwarded at carefully computed
times. For practical application of the optimization framework, we derive three dynamic algorithms, targeting at
different levels of optimality and scalability. We also present the design and implementation of our Bulk Data
Transfer (BDT) system, based on the Beacon platform and OpenFlow APIs. Experiments with realistic settings
verify the practicality of the design and the efficiency of the three algorithms, based on extensive comparisons with
schemes in the literature.
REFERENCES
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